Research map / Learning theory and algorithms / Prediction and decision losses
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CRF_RAND instead of the other evaluated methods | Structured prediction on synthetic DAGs, trees, and sets · Synthetic DAGs, trees, and sets | test set CRF loss | Helped |
| Use CRF_RAND instead of the other evaluated methods | Structured prediction on synthetic DAGs, trees, and sets · Synthetic DAGs, trees, and sets | test set Hamming loss | Helped |
| Use CRF_RAND instead of CRF_ALL | Structured prediction on synthetic DAGs · Synthetic DAGs | training time: ≈ 6 times faster | Helped |
| Use CRF_RAND instead of CRF_ALL | Structured prediction on synthetic trees · Synthetic trees | training time: ≈ 20 times faster | Helped |
| Use CRF_RAND instead of CRF_ALL | Structured prediction on synthetic sets · Synthetic sets | training time: ≈ 3 times faster | Helped |
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